Daliana's Game – Details, episodes & analysis

Daliana interviewed 6 data scientists from her meetup in New York City. It's a unique episode where you get to hear the real frustrations of data scientists. We talked about struggles working in healthcare, finance, data quality and AI, how to advocate for yourself, and align with your managers.

Subscribe to Daliana's newsletter on ⁠www.dalianaliu.com⁠ for more on data science and career.

Daliana's Twitter: ⁠https://twitter.com/DalianaLiu⁠

Daliana’s LinkedIn: ⁠https://www.linkedin.com/in/dalianaliu/

Most experimentations fail, Kristi Angel shares her expertise on scaling experimentation and avoiding common A/B testing pitfalls. Learn five things that can help boost test velocity, designing impactful experiments, and leveraging knowledge repos. (Chapters below)

Kristi Angel’s LinkedIn: ⁠https://www.linkedin.com/in/kristiangel/

Subscribe to Daliana's newsletter on ⁠www.dalianaliu.com⁠ for more on data science and career.

Daliana's Twitter: ⁠https://twitter.com/DalianaLiu⁠

Daliana’s LinkedIn: ⁠https://www.linkedin.com/in/dalianaliu/⁠

(00:00:00) Intro

(00:01:26) Why do most experimentations fail?

(00:07:05) Mistakes in choosing metrics

(00:10:05) Is revenue a good metric?

(00:13:18) Split metrics in three ways

(00:15:10) Daliana's story with too many category breakdowns

(00:16:59) What makes the best data science team?

(00:19:24) Data scientist work in silo vs in a data science team

(00:21:15) Building a knowledge center

(00:23:40) Example of knowledge center; nuance of experimentations

(00:26:09) How many metrics and variants?

(00:30:56) How to reduce noise - CUPED

(00:33:01) Future of A/B testing

(00:38:33) Q&A: Low statistical power

Jenny Wu is a data analyst turned sales engineer for data products at Hex. We talked about sales engineer vs data analyst, how to design a career based on your personality, and how to transition into a customer-facing role.

Jenny’s LinkedIn: https://www.linkedin.com/in/jenny-wu-...Daliana's Twitter: https://twitter.com/DalianaLiuDaliana’s LinkedIn: https://www.linkedin.com/in/dalianaliu/

(00:00:00) Introduction(00:01:34) What is a Sales Engineer?(00:09:35) Sales Engineering Day-to-Day(00:13:09) Challenge in sales(00:21:37) Traits of Successful Salespeople(00:30:32) Stakeholder Engagement (00:36:24) Getting into customer-facing roles(00:43:55) Quitting her job to travel the world(00:48:05) Advice on Career Breaks(00:50:39) Embedding Career and Personal Goals(00:51:57) How do you achieve happiness?

Barry McCardel is the cofounder and CEO of Hex(free trial: hex.tech/dsshow), a collaborative data workspace. Their customers include FiveTran, Notion, and Anthropic. We talked about what does the future of data team look like, how to tackle challenges of data team collaborations, and how to leverage AI in data science’s workflow.60-day Free Trial: hex.tech/dsshowBarry’s LinkedIn: https://www.linkedin.com/in/barrymccardel(00:00:00) Introduction(00:01:25) Is AI replacing data scientists?

(00:06:08) Are data science teams getting smaller?

(00:09:54) What is Hex?(00:11:24) How to communicate with stakeholders

(00:24:29) Should data scientists be full stack?

(00:31:23) How data team measure ROI(00:33:35) Quantitative vs qualitative analysis(00:35:33) When you shouldn't use data? Data vs product intuition

(00:41:39) How to hire your first data team?(00:48:59) Is the modern data stack dead?

(00:53:55) GenAI in data science workflows

(00:59:03) Future of data scientist

(01:02:30) New features in Hex

Siddhartha Sharan is a Senior Data and Applied Scientist at Microsoft, helping product teams make data-driven decisions. Currently he is working on an AI product built with OpenAI APIs for sentiment analysis. We talked about how he evaluates AI products built with large language models at Microsoft, product data science, and how he went from a business background to data science. Subscribe to Daliana's newsletter on www.dalianaliu.com for more on data science and career.

Sid’s LinkedIn: https://www.linkedin.com/in/siddharthasharan/

Daliana's Twitter: https://twitter.com/DalianaLiu

Daliana’s LinkedIn: https://www.linkedin.com/in/dalianaliu/

(00:00:00) Introduction
(00:05:20) How does Microsoft evaluate AI product
(00:16:17) Using OpenAI API for sentiment analysis
(00:25:29) Microsoft data science team culture
(00:26:52) DS, PM collaboration
(00:28:29) Three steps to build trust in data science
(00:30:13) How did he got into Microsoft
(00:34:09) Level up in Genetech
(00:36:09) ML engineer vs Product DS
(00:37:43) Core skills in product DS
(00:40:20) Hiring
(00:42:47) How to deal with burnout
(00:45:03) Should you over work to earn trust?
(00:45:44) Daliana's story about first day at Amazon
(00:49:54) Will AI replace data scientists?
(00:51:32) Data scientist's role of GenAI
(00:54:32) How to keep up with GenAI

Jess Ramos is a Senior Data Analyst at Crunchbase, a LinkedIn Learning Instructor, and a content creator in the data space. She has a bachelor's degree in Math, Spanish, and Business from Berry University and a master's in Business Analytics from University of Georgia. Today we’ll talk about SQL in the real world, data analyst vs data scientist, is job hopping bad, how she negotiated her salary. Subscribe to Daliana's newsletter on www.dalianaliu.com for more on data science and career.Jess’ Linkedin: https://www.linkedin.com/in/jessramosmsba/

Preparing to Get a Job in Data Analytics: shorturl.at/sCNPT

Solve Real-World Data Problems with SQL: https://bit.ly/3Zq6wnd

Big Data Energy Newsletter: https://bit.ly/46x4rIR

Daliana's Twitter: https://twitter.com/DalianaLiuDaliana’s LinkedIn: https://www.linkedin.com/in/dalianaliu/(00:00:00) Introduction(00:01:24) Why Jess left her job at Freddie Mac(00:03:25) Is job hopping bad(00:04:42) How to explain short job stints when interviewing(00:06:49) Jess's day-to-day work and tech stack(00:09:15) SQL in the real world(00:12:10) How to talk data to stakeholders(00:18:33) How Jess prepares for SQL interviews(00:28:11) Data analysts vs data scientists(00:32:11) Choosing a career path(00:47:19) How to ask recruiter questions(00:50:15) Jess's LinkedIn content creation journey(00:59:03) The future of Jess's career(01:03:42) Jess's favorite books

Mehdi Noori is an applied science manager at the Generative AI Innovation Center at Amazon. I used to work with Mehdi while we were at the Machine Learning Solutions Lab at AWS. So before Amazon, Mehdi was a data scientist working on marketing intelligence. Mehdi has a PhD from University of Central Florida in civil engineering and sustainability. Subscribe to Daliana's newsletter for more on data science and career www.dalianaliu.com

Mehdi Noori: https://www.linkedin.com/in/mehdi-noori/

Predicting Soccer Goals: https://aws.amazon.com/blogs/machine-learning/predicting-soccer-goals-in-near-real-time-using-computer-vision/

My friend Misty moved to a farm in Portugal after her 20 years of career in finance. We talked about her experience moving from the busy corporate life to the farm life where she does a lot of manual work. Was it challenging, how does her finance work, and what is her advice to other people who also want to explore a different path outside of the modern city life. I hope this episode will give you a different perspective about your career.

Subscribe to Daliana's newsletter on www.dalianaliu.com for more on data science and career.Daliana's Twitter: https://twitter.com/DalianaLiuDaliana’s LinkedIn: https://www.linkedin.com/in/dalianaliu/

(00:00:00) Introduction

(00:11:41) Life on the farm

(00:15:46) Her finance plans

(00:22:55) Her career journey

(00:27:14) What do accountants do

(00:32:29) I thought I would be happy

(00:41:25) Daliana's personal view about finance; when it's enough for you

(00:44:41) Does she feel lonely on a farm?

(00:48:39) What if she didn't leave the corporate world?

(00:54:07) Does she regret her decision

Pan Wu is a senior manager of data science at Meta. We talked about why he moved from machine learning to product data science, projects he worked on at Uber, Linkedin, and Meta, and how he transitioned from IC to manager. Subscribe to Daliana's newsletter on www.dalianaliu.com for more on data science and career.

Pan’s LinkedIn: https://www.linkedin.com/in/panwu/

Daliana's Twitter: https://twitter.com/DalianaLiu

Daliana’s LinkedIn: https://www.linkedin.com/in/dalianaliu/

(00:00:00) Introduction

(00:01:30) Why he transitioned from MLE to product DS

(00:07:38) Meta data scientists skill sets

(00:15:49) When did his interest shifted from MLE to product DS

(00:18:04) Is MLE more respected?

(00:25:46) A/B testing deep dives in 3 steps

(00:28:21) Built a tool at Linkedin

(00:35:52) How to sell your project

(00:41:07) Junior vs senior data scientist

(00:43:24) From staff data scientist to manager

(00:45:18) Explore being a manager

(00:46:24) Cultures in Uber, Linkedin, TrueCar

(00:52:09) Data science over the past 10 year

(00:55:06) MLE vs DS fun and frustration

(00:57:26) Product DS reality

(00:59:10) Learning new skills

(01:01:39) Mistakes he made

(01:06:34) Future of data science

(01:08:04) Will data scientists be replaced by AI

(01:09:42) Three skills he looks for when hiring

Betty Zhang is a data scientist currently working at a cloud security company, previously she was a data scientist at Amazon Web Services. Today we’ll talk about her computer vision projects in Sports, data science use cases in cyber security, from business major to data scientist, what’s her experience working in startups vs big tech companies. Subscribe to Daliana's newsletter on www.dalianaliu.com for more on data science and career.

Betty’s Linkedin: https://www.linkedin.com/in/betty-zhang-0bb63731/

Daliana's Twitter: https://twitter.com/DalianaLiu

Daliana’s LinkedIn: https://www.linkedin.com/in/dalianaliu/

(00:00:00) Introduction

(00:01:21) Computer Vision Project in Sports at AWS

(00:12:28) Challenges in computer vision

(00:14:02) Time allocation for ML projects

(00:15:22) 3 key skills for computer vision

(00:17:20) From business analyst to ML engineer

(00:18:14) How she got her data scientist job through Linkedin

(00:21:32) How she got into Amazon

(00:22:17) Three tech skills needed during Amazon interviews

(00:26:11) Why she joined a Cyber Security startup

(00:27:22) Three cybersecurity use cases

(00:29:47) Anomaly detection

(00:30:40) ML for cybersecurity

(00:34:43) Tech stacks Amazon vs Startups

(00:39:35) Startups vs big tech

(00:45:56) Balance learning and impact

(00:48:35) Advice for new data scientists